Project final report should be prepared collaboratively.

Cautionary notes: 1) Project interim reports will not be accepted after deadline. 2) You should download R Project on GitHub to your local computer, do the changes as needed, delete all the files you have not used to produce your proposal, then render the .Rmd file to .pdf (or .html output) and finally commit and push all the required files (including .Rmd files) by June 21, 2021 23:59 via GitHub Classroom of MAT381E organization.

“Image from WHO”
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Team Members

Aylin Sümer 090160324

##Tuğrulgazi Avat 090160344

Violence against women is a problem that should be perceived not only locally but also globally. The UN describes violence against women and girls (VAWG) as: “ one of the most widespread, persistent, and devastating human rights violations in our world today. It remains largely unreported due to the impunity, silence, stigma, and shame surrounding it.”

The data was taken from a survey of men and women in African, Asian, and South American countries, exploring the attitudes and perceived justifications given for committing acts of violence against women.

In this research both subjects and the objects of the violence are included. The data also explores different sociodemographic groups that the respondents belong to, including: Education Level, Marital status, Employment, and Age group. The data reveals insights into some of the attitudes and assumptions that prevent progress in the global campaign to end violence against women and girls, based on a representative sample of each country.

Data can be found in this link (it requires singing up for free in order to see it)

GOAL:

Our main purpose is to show that violence against women and girls is never acceptable or justifiable by demonstrating both numerical and categorical variables. We will be analyzing the percentages of people surveyed in the relevant group who agree with the question.

#Let’s get started!

First of all, We need to see our data. Take 6 columns for control that did we actually get the data or not.

##   RecordID     Country Gender Demographics.Question
## 1        1 Afghanistan      F        Marital status
## 2        1 Afghanistan      F             Education
## 3        1 Afghanistan      F             Education
## 4        1 Afghanistan      F             Education
## 5        1 Afghanistan      F        Marital status
## 6        1 Afghanistan      F            Employment
##          Demographics.Response                  Question Survey.Year Value
## 1                Never married ... if she burns the food  01/01/2015    NA
## 2                       Higher ... if she burns the food  01/01/2015  10.1
## 3                    Secondary ... if she burns the food  01/01/2015  13.7
## 4                      Primary ... if she burns the food  01/01/2015  13.8
## 5 Widowed, divorced, separated ... if she burns the food  01/01/2015  13.8
## 6            Employed for kind ... if she burns the food  01/01/2015  17.0

Our data has a column named as Demographics.Question which gives us the 5 cases that we are going to analyze in the future. Look at the Demographics.Question “Age”.

##   RecordID     Country Gender Demographics.Question Demographics.Response
## 1        1 Afghanistan      F                   Age                 15-24
## 2        1 Afghanistan      F                   Age                 25-34
## 3        1 Afghanistan      F                   Age                 35-49
## 4        1 Afghanistan      M                   Age                 25-34
## 5        1 Afghanistan      M                   Age                 35-49
## 6        1 Afghanistan      M                   Age                 15-24
##                    Question Survey.Year Value
## 1 ... if she burns the food  01/01/2015  17.3
## 2 ... if she burns the food  01/01/2015  18.2
## 3 ... if she burns the food  01/01/2015  18.8
## 4 ... if she burns the food  01/01/2015   8.2
## 5 ... if she burns the food  01/01/2015   8.6
## 6 ... if she burns the food  01/01/2015   9.4

We can see that age is repeating itself by the change in the response of our basic question. Our Age interval is below.

##   Demographics.Response   n
## 1                 15-24 840
## 2                 25-34 840
## 3                 35-49 840

And our Questions are;

##                                  Question    n
## 1    ... for at least one specific reason 2100
## 2              ... if she argues with him 2100
## 3               ... if she burns the food 2100
## 4 ... if she goes out without telling him 2100
## 5        ... if she neglects the children 2100
## 6 ... if she refuses to have sex with him 2100

So, we see that we have exactly 6 different answer, and 3 different age group. Let us see how many different demographics question we have.

##   Demographics.Question    n
## 1                   Age 2520
## 2             Education 3360
## 3            Employment 2520
## 4        Marital status 2520
## 5             Residence 1680

First and most important thing is that we need to make a gender study upon this data. So, We need to split our data into 2, such as the person who answered is female nor male.

Female data;

##   RecordID     Country Gender Demographics.Question
## 1        1 Afghanistan      F        Marital status
## 2        1 Afghanistan      F             Education
## 3        1 Afghanistan      F             Education
## 4        1 Afghanistan      F             Education
## 5        1 Afghanistan      F        Marital status
## 6        1 Afghanistan      F            Employment
##          Demographics.Response                  Question Survey.Year Value
## 1                Never married ... if she burns the food  01/01/2015    NA
## 2                       Higher ... if she burns the food  01/01/2015  10.1
## 3                    Secondary ... if she burns the food  01/01/2015  13.7
## 4                      Primary ... if she burns the food  01/01/2015  13.8
## 5 Widowed, divorced, separated ... if she burns the food  01/01/2015  13.8
## 6            Employed for kind ... if she burns the food  01/01/2015  17.0

Male data;

##   RecordID     Country Gender Demographics.Question
## 1        1 Afghanistan      M        Marital status
## 2        1 Afghanistan      M             Education
## 3        1 Afghanistan      M             Residence
## 4        1 Afghanistan      M            Employment
## 5        1 Afghanistan      M             Education
## 6        1 Afghanistan      M        Marital status
##          Demographics.Response                  Question Survey.Year Value
## 1                Never married ... if she burns the food  01/01/2015    NA
## 2                       Higher ... if she burns the food  01/01/2015   4.5
## 3                        Urban ... if she burns the food  01/01/2015   4.6
## 4                   Unemployed ... if she burns the food  01/01/2015   5.2
## 5                      Primary ... if she burns the food  01/01/2015   6.3
## 6 Widowed, divorced, separated ... if she burns the food  01/01/2015   6.3

And by the way, which countries are we working with,

##  [1] "Afghanistan"               "Albania"                  
##  [3] "Angola"                    "Armenia"                  
##  [5] "Azerbaijan"                "Bangladesh"               
##  [7] "Benin"                     "Bolivia"                  
##  [9] "Burkina Faso"              "Burundi"                  
## [11] "Cambodia"                  "Cameroon"                 
## [13] "Chad"                      "Colombia"                 
## [15] "Comoros"                   "Congo"                    
## [17] "Congo Democratic Republic" "Cote d'Ivoire"            
## [19] "Dominican Republic"        "Egypt"                    
## [21] "Eritrea"                   "Eswatini"                 
## [23] "Ethiopia"                  "Gabon"                    
## [25] "Gambia"                    "Ghana"                    
## [27] "Guatemala"                 "Guinea"                   
## [29] "Guyana"                    "Haiti"                    
## [31] "Honduras"                  "India"                    
## [33] "Indonesia"                 "Jordan"                   
## [35] "Kenya"                     "Kyrgyz Republic"          
## [37] "Lesotho"                   "Liberia"                  
## [39] "Madagascar"                "Malawi"                   
## [41] "Maldives"                  "Mali"                     
## [43] "Moldova"                   "Morocco"                  
## [45] "Mozambique"                "Myanmar"                  
## [47] "Namibia"                   "Nepal"                    
## [49] "Nicaragua"                 "Niger"                    
## [51] "Nigeria"                   "Pakistan"                 
## [53] "Peru"                      "Philippines"              
## [55] "Rwanda"                    "Sao Tome and Principe"    
## [57] "Senegal"                   "Sierra Leone"             
## [59] "South Africa"              "Tajikistan"               
## [61] "Tanzania"                  "Timor-Leste"              
## [63] "Togo"                      "Turkey"                   
## [65] "Turkmenistan"              "Uganda"                   
## [67] "Ukraine"                   "Yemen"                    
## [69] "Zambia"                    "Zimbabwe"

We have 5 different case, so we may actually divide our data through demographics questions.

For Female;
## RecordID Country Gender Demographics.Question Demographics.Response ## 1 1 Afghanistan F Age 15-24 ## 2 1 Afghanistan F Age 25-34 ## 3 1 Afghanistan F Age 35-49 ## 4 351 Afghanistan F Age 15-24 ## 5 351 Afghanistan F Age 25-34 ## 6 351 Afghanistan F Age 35-49 ## Question Survey.Year Value ## 1 ... if she burns the food 01/01/2015 17.3 ## 2 ... if she burns the food 01/01/2015 18.2 ## 3 ... if she burns the food 01/01/2015 18.8 ## 4 ... for at least one specific reason 01/01/2015 80.1 ## 5 ... for at least one specific reason 01/01/2015 81.5 ## 6 ... for at least one specific reason 01/01/2015 79.0
## RecordID Country Gender Demographics.Question Demographics.Response ## 1 1 Afghanistan F Education Higher ## 2 1 Afghanistan F Education Secondary ## 3 1 Afghanistan F Education Primary ## 4 1 Afghanistan F Education No education ## 5 351 Afghanistan F Education Higher ## 6 351 Afghanistan F Education No education ## Question Survey.Year Value ## 1 ... if she burns the food 01/01/2015 10.1 ## 2 ... if she burns the food 01/01/2015 13.7 ## 3 ... if she burns the food 01/01/2015 13.8 ## 4 ... if she burns the food 01/01/2015 19.1 ## 5 ... for at least one specific reason 01/01/2015 61.1 ## 6 ... for at least one specific reason 01/01/2015 81.0
## RecordID Country Gender Demographics.Question Demographics.Response ## 1 1 Afghanistan F Employment Employed for kind ## 2 1 Afghanistan F Employment Unemployed ## 3 1 Afghanistan F Employment Employed for cash ## 4 351 Afghanistan F Employment Employed for cash ## 5 351 Afghanistan F Employment Employed for kind ## 6 351 Afghanistan F Employment Unemployed ## Question Survey.Year Value ## 1 ... if she burns the food 01/01/2015 17.0 ## 2 ... if she burns the food 01/01/2015 18.0 ## 3 ... if she burns the food 01/01/2015 20.8 ## 4 ... for at least one specific reason 01/01/2015 80.2 ## 5 ... for at least one specific reason 01/01/2015 86.9 ## 6 ... for at least one specific reason 01/01/2015 80.1
## RecordID Country Gender Demographics.Question ## 1 1 Afghanistan F Marital status ## 2 1 Afghanistan F Marital status ## 3 1 Afghanistan F Marital status ## 4 351 Afghanistan F Marital status ## 5 351 Afghanistan F Marital status ## 6 351 Afghanistan F Marital status ## Demographics.Response Question Survey.Year ## 1 Never married ... if she burns the food 01/01/2015 ## 2 Widowed, divorced, separated ... if she burns the food 01/01/2015 ## 3 Married or living together ... if she burns the food 01/01/2015 ## 4 Married or living together ... for at least one specific reason 01/01/2015 ## 5 Never married ... for at least one specific reason 01/01/2015 ## 6 Widowed, divorced, separated ... for at least one specific reason 01/01/2015 ## Value ## 1 NA ## 2 13.8 ## 3 18.3 ## 4 80.6 ## 5 NA ## 6 67.6
## RecordID Country Gender Demographics.Question Demographics.Response ## 1 1 Afghanistan F Residence Rural ## 2 1 Afghanistan F Residence Urban ## 3 351 Afghanistan F Residence Rural ## 4 351 Afghanistan F Residence Urban ## 5 71 Afghanistan F Residence Rural ## 6 71 Afghanistan F Residence Urban ## Question Survey.Year Value ## 1 ... if she burns the food 01/01/2015 18.1 ## 2 ... if she burns the food 01/01/2015 18.3 ## 3 ... for at least one specific reason 01/01/2015 82.1 ## 4 ... for at least one specific reason 01/01/2015 74.0 ## 5 ... if she argues with him 01/01/2015 60.6 ## 6 ... if she argues with him 01/01/2015 54.7

For Male;

##   RecordID     Country Gender Demographics.Question Demographics.Response
## 1        1 Afghanistan      M                   Age                 25-34
## 2        1 Afghanistan      M                   Age                 35-49
## 3        1 Afghanistan      M                   Age                 15-24
## 4      351 Afghanistan      M                   Age                 15-24
## 5      351 Afghanistan      M                   Age                 25-34
## 6      351 Afghanistan      M                   Age                 35-49
##                               Question Survey.Year Value
## 1            ... if she burns the food  01/01/2015   8.2
## 2            ... if she burns the food  01/01/2015   8.6
## 3            ... if she burns the food  01/01/2015   9.4
## 4 ... for at least one specific reason  01/01/2015  74.1
## 5 ... for at least one specific reason  01/01/2015  70.5
## 6 ... for at least one specific reason  01/01/2015  73.6
##   RecordID     Country Gender Demographics.Question Demographics.Response
## 1        1 Afghanistan      M             Education                Higher
## 2        1 Afghanistan      M             Education               Primary
## 3        1 Afghanistan      M             Education             Secondary
## 4        1 Afghanistan      M             Education          No education
## 5      351 Afghanistan      M             Education                Higher
## 6      351 Afghanistan      M             Education          No education
##                               Question Survey.Year Value
## 1            ... if she burns the food  01/01/2015   4.5
## 2            ... if she burns the food  01/01/2015   6.3
## 3            ... if she burns the food  01/01/2015   7.1
## 4            ... if she burns the food  01/01/2015  10.6
## 5 ... for at least one specific reason  01/01/2015  55.5
## 6 ... for at least one specific reason  01/01/2015  77.4
##   RecordID     Country Gender Demographics.Question Demographics.Response
## 1        1 Afghanistan      M            Employment            Unemployed
## 2        1 Afghanistan      M            Employment     Employed for cash
## 3        1 Afghanistan      M            Employment     Employed for kind
## 4      351 Afghanistan      M            Employment     Employed for cash
## 5      351 Afghanistan      M            Employment     Employed for kind
## 6      351 Afghanistan      M            Employment            Unemployed
##                               Question Survey.Year Value
## 1            ... if she burns the food  01/01/2015   5.2
## 2            ... if she burns the food  01/01/2015   8.4
## 3            ... if she burns the food  01/01/2015  10.7
## 4 ... for at least one specific reason  01/01/2015  72.6
## 5 ... for at least one specific reason  01/01/2015  73.5
## 6 ... for at least one specific reason  01/01/2015  67.4
##   RecordID     Country Gender Demographics.Question
## 1        1 Afghanistan      M        Marital status
## 2        1 Afghanistan      M        Marital status
## 3        1 Afghanistan      M        Marital status
## 4      351 Afghanistan      M        Marital status
## 5      351 Afghanistan      M        Marital status
## 6      351 Afghanistan      M        Marital status
##          Demographics.Response                             Question Survey.Year
## 1                Never married            ... if she burns the food  01/01/2015
## 2 Widowed, divorced, separated            ... if she burns the food  01/01/2015
## 3   Married or living together            ... if she burns the food  01/01/2015
## 4   Married or living together ... for at least one specific reason  01/01/2015
## 5                Never married ... for at least one specific reason  01/01/2015
## 6 Widowed, divorced, separated ... for at least one specific reason  01/01/2015
##   Value
## 1    NA
## 2   6.3
## 3   8.5
## 4  72.6
## 5    NA
## 6  48.8
##   RecordID     Country Gender Demographics.Question Demographics.Response
## 1        1 Afghanistan      M             Residence                 Urban
## 2        1 Afghanistan      M             Residence                 Rural
## 3      351 Afghanistan      M             Residence                 Rural
## 4      351 Afghanistan      M             Residence                 Urban
## 5       71 Afghanistan      M             Residence                 Rural
## 6       71 Afghanistan      M             Residence                 Urban
##                               Question Survey.Year Value
## 1            ... if she burns the food  01/01/2015   4.6
## 2            ... if she burns the food  01/01/2015   9.7
## 3 ... for at least one specific reason  01/01/2015  76.1
## 4 ... for at least one specific reason  01/01/2015  59.9
## 5           ... if she argues with him  01/01/2015  47.0
## 6           ... if she argues with him  01/01/2015  40.4

Value column make us understand that the % of people surveyed in the relevant group who agree with the question (e.g. the percentage of women aged 15-24 in Afghanistan who agree that a husband is justified in hitting or beating his wife if she burns the food)

#For Female

Female Age-Value

## Warning: Removed 15 rows containing missing values (geom_bar).

We see above that Congo Democratic Republic, Chad, Afghanistan are the three of the most valued countries. And also Dominican Republic, Colombia, Peru are the three of the least valued countries. Now on, we will discuss about the three most, the there least and Turkey graphs.

###Female responders, Turkey, classified by: Age

###Female responders, Colombia, classified by: Age

###Female responders, Dominican Republic, classified by: Age

###Female responders, Peru, classified by: Age

###Female responders, Congo Democratic Republic, classified by: Age

###Female responders, Chad, classified by: Age

###Female responders, Afghanistan, classified by: Age

##Female Education-Value

## Warning: Removed 56 rows containing missing values (geom_bar).

###Female responders, Turkey, classified by: Education

###Female responders, Colombia, classified by: Education

###Female responders, Dominican Republic, classified by: Education

###Female responders, Peru, classified by: Education

###Female responders, Congo Demicratic Republic, classified by: Education

###Female responders, Chad, classified by: Education

###Female responders, Afghanistan, classified by: Education

##Female Employment-Value

## Warning: Removed 32 rows containing missing values (geom_bar).

###Female responders, Turkey, classified by: Employement

## Warning: Removed 12 rows containing missing values (geom_bar).

###Female responders, Colombia, classified by: Employement

###Female responders, Dominician Republic, classified by: Employement

###Female responders, Peru, classified by: Employement

  Female_Employment %>%
  filter(Country=="Peru")%>%
  ggplot(aes(x=Question, y=Value, fill=Demographics.Response))+
  geom_bar(position="dodge", stat = "Identity")+
  coord_flip()

###Female responders, Congo Democratic Republic, classified by: Employement

###Female responders, Chad, classified by: Employement

###Female responders, Afghanistan, classified by: Employement

##Female Marital-Value

## Warning: Removed 44 rows containing missing values (geom_bar).

###Female responders, Turkey, classified by: Marital

###Female responders, Colombia, classified by: Marital

###Female responders, Dominican Republic, classified by: Marital

###Female responders, Peru, classified by: Marital

###Female responders, Congo Democratic Republic, classified by: Marital

###Female responders, Chad, classified by: Marital

###Female responders, Afghanistan, classified by: Marital

## Warning: Removed 6 rows containing missing values (geom_bar).

##Female Residence-Value

## Warning: Removed 10 rows containing missing values (geom_bar).

###Female responders, Turkey, classified by: Residence

###Female responders, Colombia, classified by: Residence

###Female responders, Dominican Republic, classified by: Residence

###Female responders, Peru, classified by: Residence

###Female responded, Congo Demogratic Republic, classified by: Residence

###Female responders, Chad, classified by: Residence

###Female responders, Afghanistan, classified by: Residence

#For Male

Male Age-Value

## Warning: Removed 231 rows containing missing values (geom_bar).

We see above that Turkey, Turkmenistan, Yemen, Tajikistan,Peru, Philippines, Nicaragua, Morocco, Eritrea, Egypt and Bolivia has no information(NA values). There is no specific information in the data dictionary, whether male participants were not asked those questions or they did not answer. Because of that, We can not use the male participants in Turkey. We will continue in Turkey with female participants. Congo Democratic Republic, Chad, Afghanistan are still the three of the most valued countries. And also Dominican Republic, , Colombia, Guatemala (In female data there was Peru) are the three of the least valued countries. Now on, we will discuss about the three most, the three least and Turkey graphs.

###Male responders, Colombia, classified by: Age

###Male responders, Dominican Republic, classified by: Age

###Male responders, Guatemala, classified by: Age

###Male responders, Congo Democratic Republic, classified by: Age

###Male responders, Chad, classified by: Age

###Male responders, Afghanistan, classified by: Age

##Male Education-Value

## Warning: Removed 362 rows containing missing values (geom_bar).

###Male responders, Colombia, classified by: Education

###Male responders, Dominican Republic, classified by: Education

###Male responders, Guatemala, classified by: Education

###Male responders, Congo Democratic Republic, classified by: Education

###Male responders, Chad, classified by: Education

###Male responders, Afghanistan, classified by: Education

—

##Male Employment-Value

## Warning: Removed 254 rows containing missing values (geom_bar).

###Male responders, Colombia, classified by: Employement

###Male responders, Dominican Republic, classified by: Employement

## Warning: Removed 12 rows containing missing values (geom_bar).

###Male responders, Guatemala, classified by: Employement

— ###Male responders, Congo Democratic Republic, classified by: Employement

###Male responders, Chad, classified by: Employement

###Male responders, Afghanistan, classified by: Employement

— ##Male Marital-Value

## Warning: Removed 255 rows containing missing values (geom_bar).

###Male responders, Colombia, classified by: Marital

###Male responders, Dominican Republic, classified by: Marital

###Male responders, Guatemala, classified by: Marital

###Male responders, Congo Democratic Republic, classified by: Marital

###Male responders, Chad, classified by: Marital

###Male responders, Afghanistan, classified by: Marital

## Warning: Removed 6 rows containing missing values (geom_bar).

— ##Male Residence-Value

## Warning: Removed 154 rows containing missing values (geom_bar).

###Male responders, Colombia, classified by: Residence

###Male responders, Dominican Republic, classified by: Residence

###Male responders, Guatemala, classified by: Residence

###Male responders, Congo Democratic Republic, classified by: Residence

###Male responders, Chad, classified by: Residence

###Male responders, Afghanistan, classified by: Residence

We see many graphs above. But what do they really mean? Let’s discuss. Firstly, man population have a thing with surveys about violence. Neither they didn’t want to take it, nor they haven’t been asked such a thing. Well, this doesn’t make our work easy, Because the men’ answers (even though we can predict) do not exist in Turkey. So we had to consider only the female participants in Turkey.

First of all, there are really great number of women that justify the violence, for at least one specific reason in those countries… You may think that 15-24 ages are the one, valued the least, but some countries that is not the case, for example Uganda, Zimbabwe, in female participants. If you look at the male participants, you will see that most of the countries have higher violence value in the age 15-24, so here, we can say the exceptions; Ukraine, Albania and Kyrgyz Republic. Doesn’t it sound so wrong?

Well, let’s pass the age and look at the education. For Female participants, higher education makes the lowest violence fans. But still, some countries have the smallest different at the values such as Timor-Leste and Chad. Even they have higher education, still they think about justifying the violence. For some countries education make almost no difference in the acceptability of violence. For Male participants, for Azerbaijan, Kyrgyz Republic, Ukraine and Moldova, there exist only the participants with higher and secondary educations. We may see that participants from Timor-Leste and Kyrgyz Republic are more violent in higher education. Also Ukraine and Azerbaijan have the smallest difference between higher educated participants and non educated ones. Did your brain just blow up? Well, education has no impact of some people…

Now, look at the employment- justified violence relations. For Female participants, in Turkey, they only asked the unemployed women, so we cannot have an idea in this data to what is Turkish employed women think. For other countries, employed for kind and unemployed participants mostly have close opinion about the violence, but mostly participants that employed for kind are the most violent sympathizers. For male participants,except for Kyrgyz Republic, Timor-Leste and Azerbaijan participants who employed for kind justify violence the most. But in Kyrgyz Republic, Timor-Leste and Azerbaijan, employed for cash participants justify the most. Also for Dominican Republic, there is no data for employed people. What should we understand from here? Something like, even though they are employed they found a reason to show violence,and the unemployed ones think that they already have a reason to be violent.

Next is marital statues. For female participants, Malawi, Maldives, Madagaskar, Lesotho, Eswatini, Comoros have the most violence fans in the never been married women. Doesn’t it crazy? Afghanistan and Pakistan have no information about the never married participants’ thoughts. Also that, Burundi, Nabia, Rwanda, Philiphines have the most violent divorced/widow women population. And Turkey has a tie with married/living together part of the women vs. divorced/widow women. It sounds about right. For male participants, Comoros, Azerbaijan, Benin, Eswatini, Zambia, Zimbabwe have the most violent thoughts in the never married men… Even though Dominican Republic has the lowest values in the survey, never married men in this country justify violence more then married and divorced/widow men. Have you ever noticied that the thoughts of women and men in the same countries have almost the same graphs with close values? Just think about that…

The last part is residence. For female participants and the male participants, always urban residences have the least violence thoughts than the rural residences. Well, it looks more normal than our other split data.

If you look close enough, you can see that all informations are connected, in urban, there are more people with education, more employments, age intervals mostly around 20-40 and so on. If you look at one country at a time, you may see the relationships better. So let’s try with Turkey.

Lets look at our Turkey data one more time.

##   RecordID Country Gender Demographics.Question Demographics.Response
## 1      414  Turkey      F                   Age                 15-24
## 2      414  Turkey      F                   Age                 25-34
## 3      414  Turkey      F                   Age                 35-49
## 4      134  Turkey      F                   Age                 15-24
## 5      134  Turkey      F                   Age                 25-34
## 6      134  Turkey      F                   Age                 35-49
##                               Question Survey.Year Value
## 1 ... for at least one specific reason  01/01/2013  10.8
## 2 ... for at least one specific reason  01/01/2013  11.6
## 3 ... for at least one specific reason  01/01/2013  16.4
## 4           ... if she argues with him  01/01/2013   5.2
## 5           ... if she argues with him  01/01/2013   5.0
## 6           ... if she argues with him  01/01/2013   7.9

See below, all the Demographics Responce together.

## Warning: Removed 12 rows containing missing values (geom_bar).

But for comparison better, it looks the best when they are in seperated graphs. See below;

## Warning: Removed 12 rows containing missing values (geom_bar).

If we examine the Turkey data, some deductions may be done (Even though male datas are lacking).

Ages:

  • In 2013, over 15% of women between the ages of 35 and 49 and over 10% of women between the ages of 15 and 24 justifies the violence for at least one specific reason.
  • In every question, elder women have the highest values.

Education:

  • Let’s only focus on “no education” and “primary” to see the importance of having an education. In every category, values of females who have no education doubles the values of females who have at least primary education.
  • Also it is obvious that the higher education status a women have, the less her probability of justifying the action of violence.

Marital Status:

  • It is interesting to see that women married or living together and women widowed/divorced/separated have the similar percentages in most of the questions. However, women who never married are less tolerated when it comes to violence against them. Whether it is love or hate, it is a FAMILY which is easy to mess up but hard to form.(?)

Residence:

  • Similar to the education part, women living in rural areas are two times more likely to accept the violence comparing to the women living in urban areas.

Those were concluded from datas of females. Since we have no male datas, we would want you to imagine the missing values of males by looking at those;

  • Femicides, which means the gender-based murder of a woman by a man, had increased by 1400 percent from 2002 to 2009 and that while 66 women were killed in 2002, this number reached 953 in the first 7 months of 2009. See link

  • “2020”

  • If we consider the year 2018, it is worse than 2020. 440 women were killed and 317 women were subjected to sexual violence.

There is a very serious increase in the number of suspicious female deaths presented as suicide or natural death and the number of women who are found dead suspiciously with the pandemic process. It is necessary to reveal whether women were killed, whether they were killed by accident, whether women were killed on the basis of gender (whether it was femicide), whether they committed suicide or were driven to suicide.

For those datas and explanations, visit Kadın Cinayetlerini Durduracağız Platformu.

REFERENCES

https://data.world/login?next=%2Fmakeovermonday%2F2020w10%2Fworkspace%2Ffile%3Ffilename%3D20200306%2BData%2BInternational%2BWomen%2527s%2BDay%2BViz5%2BLaunch.csv

https://www.ntv.com.tr/yasam/kadin-cinayetleri-yuzde-1400-artis-gosterdi,8aPXnckeN0aWmKBDjlezJw

http://kadincinayetlerinidurduracagiz.net/veriler/2947/kadin-cinayetlerini-durduracagiz-platformu-2020-raporu